Reliability Assessment of Regional Climate Modeling: Case study in Johor State, Malaysia
Bibliographic record
Abstract
Abstract This paper presents the work of statistically downscaling the CAN ESM 2 (Canada Earth System Model 2) climate data into regional climate data to produce the future climate scenario using the RCP (Representative Concentration Pathways) 2.6,4.5 and 8.5 green-house gas concentration trajectory suggested by Intergovernmental Panel on Climate Change Fifth Assessment Reports (IPCC-AR5). Selected location for regional climate downscaling includes Batu Pahat (1° 52’ N 102° 59’ E) and Kulai (1° 38’ N 103° 40’ E), downscaled outcome of monthly rainfall (mm), daily maximum (Tmax) and daily minimum (Tmin) temperature (°C) was produced by using SDSM (Statistical Downscaling Model) software to calibrate the CANESM2 output with the historical data. Quantile-mapping bias correction by using exponential distribution function was done to obtain bias corrected rainfall data. Reliability test using Pearson correlation coefficient was done by comparing between actual historical data. Based on Pearson correlation applied on bias corrected results, for Batu Pahat, the most suitable RCP model for both Tmax and Tmin is RCP 2.6, with correlation of 0.74 and 0.72, most suitable model for rainfall is RCP 4.5 with correlation of 0.24. For Kulai, the most suitable RCP model for Tmin is RCP 8.5, with correlation of 0.63, for Tmax and rainfall the suitable model is RCP 2.6, with correlation of 0.73 and 0.36. In overall, the more appropriate model to describe the climate for both Batu Pahat and Kulai based on Pearson correlation from year 2006 to 2019 is RCP 2.6, as the RCP 2.6 model are having higher correlation to the historical data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".